شبکه همجوشی کانولوشنی چند مقیاسی برای بازیابی تصویر
محورهای موضوعی : Computer Engineering
1 - Department of Computer engineering, Sari Branch, Islamic Azad University, Sari, IRAN.
کلید واژه: بازیابی تصویر, استخراج ویژگی, یادگیری عمیق, فیلترهای موازی, شبکه کانولوشنی,
چکیده مقاله :
بازیابی تصویر از پایگاه‌های داده بزرگ‌مقیاس به دلیل محدودیت‌های روش‌های سنتی متنی و مبتنی بر محتوا چالشی مهم در بینایی ماشین است که اغلب منجر به ایجاد "شکاف معنایی" می‌شود. شبکه همجوشی کانولوشنی چند مقیاسی (MSCFNet) به عنوان روشی نوین برای بهبود دقت و کارایی در بازیابی تصویر معرفی شده است. این شبکه با استفاده از فیلترهایی با اندازه‌های مختلف، ویژگی‌های کوچک، متوسط و بزرگ‌مقیاس را به‌طور هم‌زمان استخراج می‌کند و یک نمایه جامع‌تر از تصاویر ارائه می‌دهد. این رویکرد باعث بهبود تشخیص الگوهای متنوع و جزئیات بصری شده و عملکرد بازیابی تصویر را افزایش می‌دهد. MSCFNet با عملیات "جمع" برای همجوشی ویژگی‌ها پیچیدگی مدل را کاهش داده و کارایی محاسباتی را حفظ می‌کند. این شبکه در دو نسخه با 2 و 4 لایه چند مقیاسی پیاده‌سازی شده و در مجموعه داده‌های CIFAR-10، CIFAR-100 و Fashion-MNIST ارزیابی شده است. نتایج نشان می‌دهد که MSCFNet نسبت به مدل‌های پیچیده‌تر مانند ResNet18 و ResNet50 عملکرد بهتری دارد و در CIFAR-10 دقت 74.43٪، در CIFAR-100 دقت 38.87٪ و در Fashion-MNIST دقت 92.47٪ به دست می‌آورد. همچنین، نسخه دو لایه با زمان آموزش 113.1 ثانیه در CIFAR-10، دقت بالا و پیچیدگی کمتری را ارائه می‌دهد. MSCFNet به دلیل تعادل در دقت و کارایی محاسباتی، برای محیط‌های با منابع محدود مناسب است.
The task of image retrieval from large-scale databases presents a major challenge in computer vision due to the limitations of traditional text-based and content-based methods in fully capturing visual features, often leading to a "semantic gap." A novel method, the Multi-Scale Convolutional Fusion Network (MSCFNet), has been introduced to improve both the accuracy and efficiency of image retrieval by employing multi-scale convolutional layers. MSCFNet uses filters of different sizes to simultaneously extract fine, medium, and large-scale features, offering a more detailed representation of images. This enables better detection of diverse patterns and visual details, enhancing image matching and retrieval performance. Additionally, MSCFNet minimizes model complexity by using the "addition" operation for feature fusion, maintaining computational efficiency without increasing feature map dimensionality. MSCFNet was implemented in two versions, one with 2 layers and another with 4 layers, and tested on CIFAR-10, CIFAR-100, and Fashion-MNIST datasets. The results show MSCFNet consistently outperforms more complex models like ResNet18 and ResNet50, with accuracies of 74.43% on CIFAR-10, 38.87% on CIFAR-100, and 92.47% on Fashion-MNIST. Furthermore, MSCFNet greatly reduces parameters and training time, with the 2-layer version requiring just 113.1 seconds on CIFAR-10 while maintaining high accuracy. The 4-layer version further improves accuracy and F-Score across all datasets. MSCFNet's balance of accuracy, efficiency, and reduced complexity makes it ideal for use in resource-limited environments.
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